Since databases became a mature technology and massive collection and storage of data became feasible at increasingly cheaper costs, a push emerged towards powerful methods for discovering knowledge from those data, capable of going beyond the limitations of traditional statistics, machine learning and database querying. This is why data mining emerged as an important multi-disciplinary field. Data mining is the process of automatically discovering useful information in large data repositories. Often, traditional data analysis tools and techniques cannot be used because of the volume of data, such as point-of-sale data, Web logs, earth observation data from satellites, genomic data, location data from telecom service providers. Sometimes, the non-traditional nature of the data implies that ordinary data analysis techniques are not applicable. Today, data mining is both a technology that blends data analysis methods with sophisticated algorithms for processing large data sets, and an active research field that aims at developing new data analysis methods for novel forms of data. This course is aimed at providing a succinct account of the foundations of data mining, together with an overview of the most advanced topics and application areas, as well as the current frontiers of data mining research. First part of the course (Data mining - foundations) covers: the basic concepts, the knowledge discovery process, mining various forms of data (relational, transactional, object-relational, spatiotemporal, text, multimedia, web, etc), mining various forms of knowledge (classification, clustering, and frequent patterns), evaluation of knowledge, and key applications of data mining. The second part of the course (Data mining - advanced concepts and case studies) gives an introductory account of the frontiers of data mining research: sequential data mining, mining data streams, web mining, social network analysis, graph and network mining, spatiotemporal data and mobility data mining, privacy-preserving data mining, together with presentations of real-life case studies in various domains, including retail and market analysis, fiscal fraud detection, transportation and mobility.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Pearson Addison-Wesley, 2006. (slides and chapters 4, 6 e 8 downloadable)
Fosca Giannotti and Dino Pedreschi (Eds.) Mobility, Data Mining and Privacy. Springer, 2008. (intro chapter downloadable)
Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques, 2nd ed. Morgan Kaufmann Publishers, 2006. (slides downloadable)
Xindong Wu et al. Top 10 algorithms in data mining. Knowledge and Information Systems (2008) 14:1–37.
Introduction to Data Mining
Frontiers of Data Mining research
The exam for this course consists of a term paper, reporting
The exam can be conducted in teams, and should be preferably close to the research interest of the candidate, exploiting the interdisciplinary nature of data mining and knowledge discovery.
The students willing to give the exam should send an email with subject [BISS09] to the instructor, specifying the chosen subject for the work, and the list of participants in the team. Once negotiated with the instructor, the assigned teamwork will be inserted in this wiki, were also the final report wil be published (in pdf format). The exam must be completed within 2009.